Methods for prediction of anti-tnf alpha drug levels and autoantibody formation

ABSTRACT

In some aspects, the present invention provides methods for predicting whether a subject will develop autoantibodies to an anti-TNFα drug during the course of anti-TNFα drug therapy. In other aspects, the present invention provides methods for predicting the level of an anti-TNFα drug in a subject during the course of anti-TNFα drug therapy. Systems for predicting anti-TNFα drug levels and the likelihood of autoantibody formation during the course of anti-TNFα drug therapy are also provided herein. The present invention further provides methods for predicting a clinical outcome (e.g., endoscopic response) of a subject on anti-TNFα drug therapy.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application is a continuation of PCT/IB32015/058048 filedOct. 19, 2015, which claims priority to U.S. Provisional Application No.62/065,997, filed Oct. 20, 2014, and U.S. Provisional Application No.62/086,103, filed Dec. 1, 2014, the disclosures of which are herebyincorporated by reference in their entirety for all purposes.

BACKGROUND OF THE INVENTION

Tumor necrosis factor alpha (TNF-α) is a cytokine produced by numerouscell types, including monocytes and macrophages, that was originallyidentified based on its ability to induce the necrosis of certain mousetumors. TNF-α has been implicated in the pathophysiology of a variety ofother human diseases and disorders, including shock, sepsis, infections,autoimmune diseases, rheumatoid arthritis, Crohn's disease, transplantrejection, and graft-versus-host disease.

Because of the harmful role of TNF-α in a variety of human diseases anddisorders, therapeutic strategies have been designed to inhibit orcounteract TNF-α activity. Antibodies that bind to, and neutralize,TNF-α have been sought as a means to inhibit TNF-α activity. Inparticular, biological therapies have been applied to the treatment ofinflammatory disorders such as Crohn's disease and autoimmune disorderssuch as rheumatoid arthritis. Examples of TNF-α inhibitors includeREMICADE™ (infliximab), ENBREL™ (etanercept), HUMIRA™ (adalimumab), andCIMZIA® (certolizumab pegol). While such biological therapies havedemonstrated success in the treatment of Crohn's disease and rheumatoidarthritis, not all subjects treated respond, or respond well, to suchtherapy. Moreover, the administration of TNF-α inhibitors can induce animmune response to the drug and lead to the production of autoantibodiessuch as human anti-chimeric antibodies (HACA), human anti-humanizedantibodies (HAHA), and human anti-mouse antibodies (HAMA). Such HACA,HAHA, or HAMA immune responses can be associated with hypersensitivereactions and dramatic changes in pharmacokinetics and biodistributionof the immunotherapeutic TNF-α inhibitor that preclude further treatmentwith the drug. Thus, there is a need in the art for selecting atherapeutic regimen with TNF-α inhibitors that is both efficacious andreduces the risk of autoantibody formation to the drug, therebyimproving patient outcomes. The present invention satisfies this needand provides related advantages as well.

BRIEF SUMMARY OF THE INVENTION

In some aspects, the present invention provides methods for predictingwhether a subject will develop autoantibodies to an anti-TNFα drugduring the course of anti-TNFα drug therapy. In other aspects, thepresent invention provides methods for predicting the level of ananti-TNFα drug in a subject during the course of anti-TNFα drug therapy.Systems for predicting anti-TNFα drug levels and the likelihood ofautoantibody formation during the course of anti-TNFα drug therapy arealso provided herein. The present invention further provides methods forpredicting a clinical outcome (e.g., endoscopic response) of a subjecton anti-TNFα drug therapy.

In one aspect, the present invention provides a method for predictingwhether a subject will develop autoantibodies to an anti-TNFα drug at alater time point during a course of therapy with the anti-TNFα drug, themethod comprising measuring the level of the anti-TNFα drug in a sampleobtained from the subject at an earlier time point during the course oftherapy.

In another aspect, the present invention provides a method forpredicting the level of an anti-TNFα drug in a subject at a later timepoint during a course of therapy with the anti-TNFα drug, the methodcomprising determining one or more predictor variables for the subjectat an earlier time point during the course of therapy and/or prior tothe initiation of the course of therapy.

In yet another aspect, the present invention provides a method forpredicting whether a subject will develop autoantibodies to an anti-TNFαdrug at a later time point during a course of therapy with the anti-TNFαdrug, the method comprising determining one or more predictor variablesfor the subject at an earlier time point during the course of therapyand/or prior to the initiation of the course of therapy.

In an additional aspect, the present invention provides a system forpredicting the level of an anti-TNFα drug in a subject at a later timepoint during a course of therapy with the anti-TNFα drug, the systemcomprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising one or more predictor variables for the subject        determined at an earlier time point during the course of therapy        and/or prior to the initiation of the course of therapy;    -   (b) a data processing module configured to process the data set        by applying a statistical analysis to the data set to produce a        statistically derived decision predicting the level of the        anti-TNFα drug based upon the one or more predictor variables;        and    -   (c) a display module configured to display the statistically        derived decision.

In a further aspect, the present invention provides a system forpredicting whether a subject will develop autoantibodies to an anti-TNFαdrug at a later time point during a course of therapy with the anti-TNFαdrug, the system comprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising one or more predictor variables for the subject        determined at an earlier time point during the course of therapy        and/or prior to the initiation of the course of therapy;    -   (b) a data processing module configured to process the data set        by applying a statistical analysis to the data set to produce a        statistically derived decision predicting whether the subject        will develop autoantibodies to the anti-TNFα drug based upon the        one or more predictor variables; and    -   (c) a display module configured to display the statistically        derived decision.

In another aspect, the present invention provides a method forpredicting a clinical outcome of a subject at a later time point duringa course of therapy with the anti-TNFα drug, the method comprisingdetermining one or more predictor variables selected from the level ofIL12p40, the level of IL-8, the level of the anti-TNFα drug, andcombinations thereof in a sample obtained from the subject at an earliertime point during the course of therapy.

In yet another aspect, the present invention provides a method forpredicting whether a subject will develop autoantibodies to an anti-TNFαdrug at a later time point during a course of therapy with the anti-TNFαdrug, the method comprising determining one or more predictor variablesselected from the level of IL-8, the level of the anti-TNFα drug, theTNFα/drug ratio, and combinations thereof in a sample obtained from thesubject at an earlier time point during the course of therapy.

Other objects, features, and advantages of the present invention will beapparent to one of skill in the art from the following detaileddescription and figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C show the relationship between IFX levels and the ATIformation in CD patients at week 2 (FIG. 1A), week 6 (FIG. 1B), and week14 (FIG. 1C) following the initiation of IFX therapy (baseline or week0).

FIG. 2 shows the association between TNFα, IFX, C-reactive protein(CRP), and human serum albumin (HSA) with ATI formation (p-values) atbaseline (week 0), and at weeks 2, 6, and 14 following IFX therapy.

FIG. 3 shows a stratified analysis of the association between IFX levelsand ATI formation in CD patients receiving IFX monotherapy orcombination therapy with IFX and an immunosuppressive agent.

FIG. 4 shows the results of a quartile analysis that was performed tofurther characterize the association between IFX levels at week 2 andATI formation at week 14.

FIG. 5 shows the results of a quartile analysis that was performed tofurther characterize the association between IFX levels at week 6 andATI formation at week 14.

FIG. 6 shows the results of a quartile analysis that was performed tofurther characterize the association between IFX levels at week 14 andATI formation at week 14.

FIG. 7 shows the results of multiple regression modelling to predict IFXlevels at week 14 using baseline measures of initial predictorvariables.

FIG. 8 shows the results of multiple regression modelling to predict IFXlevels at week 2 using baseline measures of initial predictor variables.

FIG. 9 shows the results of multiple regression modelling to predict IFXlevels at week 6 using baseline and week 2 measures of initial predictorvariables.

FIG. 10 shows the results of multiple regression modelling to predictIFX levels at week 14 using baseline, week 2, and week 6 measures ofinitial predictor variables.

FIG. 11 shows the results of multiple regression modelling to predictIFX levels at week 14 using baseline, week 2, and week 6 measures ofinitial predictor variables, but enforcing TNFα in the model.

FIG. 12 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using baseline measures of initialpredictor variables.

FIG. 13 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using baseline and week 2 measures ofinitial predictor variables.

FIG. 14 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using baseline, week 2, and week 6measures of initial predictor variables.

FIG. 15 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using all time point measurements ofinitial predictor variables.

FIG. 16 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using all time point measurements ofinitial predictor variables including TNFα/IFX ratios.

FIGS. 17A-17D show the relationship between TNFα levels at baseline(FIG. 17A), week 2 (FIG. 17B), week 6 (FIG. 17C), and week 14 (FIG. 17D)and ATI formation at week 14.

FIG. 18 shows the association between TNFα levels at baseline and IFXlevels at weeks 2, 6, and 14.

FIG. 19 shows a stratified analysis of the association between baselineTNFα levels and IFX levels in CD patients receiving IFX monotherapy orcombination therapy with IFX and an immunosuppressive agent.

FIG. 20 shows the association between HSA levels and TNFα levels.

FIG. 21 shows the association between CRP levels and TNFα levels.

FIG. 22 shows the association between TNFα/IFX ratios and CRP levels.

FIG. 23 shows a stratified analysis of the association between ratios ofbaseline TNFα levels to IFX levels at different time points and CRPlevels at week 14 in CD patients receiving IFX monotherapy orcombination therapy with IFX and an immunosuppressive agent.

FIG. 24 shows a stratified analysis of the association between ratios ofTNFα levels to IFX levels at different time points and CRP levels atweek 14 in CD patients receiving IFX monotherapy or combination therapywith IFX and an immunosuppressive agent.

FIG. 25 shows a stratified analysis of the association between ratios ofTNFα levels to IFX levels at different time points and ATI formation atweek 14 in CD patients receiving IFX monotherapy or combination therapywith IFX and an immunosuppressive agent.

FIG. 26 shows the association between IFX levels and CRP levels.

FIG. 27 shows the association between baseline HSA levels and IFX levelsduring the course of therapy.

FIG. 28 shows the association between CRP levels and HSA levels atbaseline and at different time points during the course of therapy.

FIG. 29 shows the association between IL12p40 levels at T5 (week 2) andendoscopic response at week 8.

FIG. 30 shows the association between IL-8 levels at T5 (week 2) andendoscopic response at week 8.

FIG. 31 shows the association between IFX drug levels at TO (24 hoursafter dosing) and endoscopic response at week 8.

FIG. 32 shows the results of multiple regression modelling to predictclinical outcome (e.g., endoscopic response) at week 8.

FIG. 33 shows the association between IL-8 levels at T5 (week 2) and ATIformation at T9 (i.e., by week 6 or within the first 6 weeks of IFXtherapy).

FIG. 34 shows the association between IFX levels at TO (24 hours afterdosing) and ATI formation at T9 (i.e., by week 6 or within the first 6weeks of IFX therapy).

FIG. 35 shows the association between the ratio of TNFα levels to IFXlevels (i.e., TNFα/IFX ratio) at TO (24 hours after dosing) and ATIformation at T9 (i.e., by week 6 or within the first 6 weeks of IFXtherapy).

FIG. 36 shows the results of multiple regression modelling using IL-8levels together with IFX levels to predict ATI formation at T9 (i.e., byweek 6 or within the first 6 weeks of IFX therapy).

FIG. 37 shows the results of multiple regression modelling using IL-8levels together with TNFα/IFX ratio to predict ATI formation at T9(i.e., by week 6 or within the first 6 weeks of IFX therapy).

DETAILED DESCRIPTION OF THE INVENTION I. Introduction

The present invention provides methods for predicting whether a subjectwill develop autoantibodies to an anti-TNFα drug during the course ofanti-TNFα drug therapy. The present invention also provides methods forpredicting the level of an anti-TNFα drug in a subject during the courseof anti-TNFα drug therapy. The present invention further providessystems for predicting anti-TNFα drug levels and the likelihood ofautoantibody formation during the course of anti-TNFα drug therapy. Thepresent invention also provides methods for predicting a clinicaloutcome (e.g., endoscopic response) of a subject on anti-TNFα drugtherapy.

In certain aspects, the examples described herein demonstrate that thelevel of an anti-TNFα drug (e.g., IFX) at an earlier time point duringtherapy is predictive of anti-TNFα drug autoantibody (e.g., ATI)formation at a later time point during therapy. In other aspects, theexamples described herein demonstrate that anti-TNFα drug (e.g., IFX)levels above a specific reference level or cut-off value (i.e., druglevels in the 4^(th) quartile or Q4 based on quartile analysis) at anearlier time point during therapy is predictive of whether a patientwill develop anti-TNFα drug autoantibody (e.g., ATI) at a later timepoint during therapy.

In certain aspects, the examples described herein demonstrate that theinitial dose of an anti-TNFα drug (e.g., IFX) can be individualized andtailored for each patient at the start of therapy based on the use ofpredictive models such as multiple regression models. In other aspects,the examples described herein demonstrate that patients predicted todevelop anti-TNFα drug autoantibody (e.g., ATI) during the course ofanti-TNFα drug (e.g., IFX) therapy based on the use of predictive modelscan be administered an initial dose of the drug that is increasedcompared to the normal starting dose and/or an increased dose of animmunosuppressive agent such as azathioprine (AZA), 6-mercaptopurine(6-MP), or methotrexate (MTX).

In certain aspects, the examples described herein demonstrate thatbiomarkers such as IL12p40, IL-8, and anti-TNFα drug (e.g., IFX) at oneor more earlier time points during the course of anti-TNFα drug (e.g.,IFX) therapy are predictive of clinical outcome (e.g., endoscopicresponse) at a later time point during therapy. In certain embodiments,the level of IL12p40 and/or IL-8 at week 2 can be used to predictclinical outcome (e.g., endoscopic response) at week 8 of anti-TNFα drug(e.g., IFX) therapy. In other embodiments, the level of IFX at 24 hoursafter dosing can be used to predict clinical outcome (e.g., endoscopicresponse) at week 8 of anti-TNFα drug (e.g., IFX) therapy. In otheraspects, the examples described herein demonstrate that biomarkers suchas IL-8 and anti-TNFα drug (e.g., IFX) as well as a ratio of TNFα toanti-TNFα drug (e.g., IFX) at one or more earlier time points during thecourse of anti-TNFα drug (e.g., IFX) therapy are predictive of anti-TNFαdrug autoantibody (e.g., ATI) formation at a later time point duringtherapy. In certain embodiments, the level of IL-8 at week 2, the levelof IFX at 24 hours after dosing, and/or the ratio of TNFα level to IFXlevel (i.e., TNFα/IFX ratio) at 24 hours after dosing can be used topredict anti-TNFα drug autoantibody (e.g., ATI) formation at week 6 ofanti-TNFα drug (e.g., IFX) therapy (i.e., by week 6 or within the first6 weeks of therapy).

II. Definitions

As used herein, the following terms have the meanings ascribed to themunless specified otherwise.

The terms “a,” “an,” or “the” as used herein not only include aspectswith one member, but also include aspects with more than one member. Forinstance, the singular forms “a,” “an,” and “the” include pluralreferents unless the context clearly dictates otherwise. Thus, forexample, reference to “a cell” includes a plurality of such cells andreference to “the agent” includes reference to one or more agents knownto those skilled in the art, and so forth.

The term “course of therapy” includes any therapeutic approach taken torelieve or prevent one or more symptoms associated with a TNFα-mediateddisease or disorder. The term encompasses administering any compound,drug, procedure, and/or regimen useful for improving the health of anindividual with a TNFα-mediated disease or disorder and includes any ofthe therapeutic agents described herein. One skilled in the art willappreciate that either the course of therapy or the dose of the currentcourse of therapy can be changed (e.g., increased or decreased) usingthe methods and systems of the present invention.

The term “TNFα” is intended to include a human cytokine that exists as a17 kDa secreted form and a 26 kDa membrane associated form, thebiologically active form of which is composed of a trimer ofnoncovalently bound 17 kDa molecules. The structure of TNFα is describedfurther in, for example, Jones et al., Nature, 338:225-228 (1989). Theterm TNFα is intended to include human TNFα, a recombinant human TNFα(rhTNF-α), or TNFα that is at least about 80% identity to the human TNFαprotein. Human TNFα consists of a 35 amino acid (aa) cytoplasmic domain,a 21 aa transmembrane segment, and a 177 aa extracellular domain (ECD)(Pennica, D. et al. (1984) Nature 312:724). Within the ECD, human TNFαshares 97% aa sequence identity with rhesus TNFα, and 71% to 92% aasequence identity with bovine, canine, cotton rat, equine, feline,mouse, porcine, and rat TNFα. TNFα can be prepared by standardrecombinant expression methods or purchased commercially (R & D Systems,Catalog No. 210-TA, Minneapolis, Minn.).

In certain embodiments, “TNFα” is an “antigen,” which includes amolecule or a portion of the molecule capable of being bound by ananti-TNF-α drug. TNFα can have one or more than one epitope. In certaininstances, TNFα will react, in a highly selective manner, with ananti-TNFα antibody. Preferred antigens that bind antibodies, fragments,and regions of anti-TNFα antibodies include at least 5 amino acids ofhuman TNFα. In certain instances, TNFα is a sufficient length having anepitope of TNFα that is capable of binding anti-TNFα antibodies,fragments, and regions thereof.

The terms “anti-TNFα drug” or “TNFα inhibitor” as used herein areintended to encompass agents including proteins, antibodies, antibodyfragments, fusion proteins (e.g., Ig fusion proteins or Fc fusionproteins), multivalent binding proteins (e.g., DVD Ig), small moleculeTNFα antagonists and similar naturally- or nonnaturally-occurringmolecules, and/or recombinant and/or engineered forms thereof, that,directly or indirectly, inhibits TNFα activity, such as by inhibitinginteraction of TNFα with a cell surface receptor for TNFα, inhibitingTNFα protein production, inhibiting TNFα gene expression, inhibitingTNFα secretion from cells, inhibiting TNFα receptor signaling or anyother means resulting in decreased TNF-α activity in a subject. The term“anti-TNFα drug” or “TNFα inhibitor” preferably includes agents whichinterfere with TNFα activity. Examples of anti-TNFα drugs include,without limitation, infliximab (REMICADE™, Johnson and Johnson), humananti-TNF monoclonal antibody adalimumab (D2E7/HUMIRA™, AbbottLaboratories), etanercept (ENBREL™, Amgen), human anti-TNF monoclonalantibody golimumab (SIMPONI®, CNTO 148), CDP 571 (Celltech), andpegylated Fab′ fragment of a humanized TNF inhibitor monoclonal antibody(certolizumab pegol (CIMZIA®, UCB, Inc.), as well as other compoundswhich inhibit TNFα activity, such that when administered to a subjectsuffering from or at risk of suffering from a disorder in which TNFαactivity is detrimental (e.g., IBD or clinical subtype thereof such asCD), the disorder is treated.

The terms “anti-drug antibody” and “ADA” are intended to encompass ahuman anti-chimeric antibody (HACA), a human anti-humanized antibody(HAHA), and a human anti-mouse antibody (HAMA). The terms “antibodies toinfliximab” and “ATI” refer to antibodies against the anti-TNFα antibodydrug infliximab.

The term “co-administer” includes to administer more than one activeagent, such that the duration of physiological effect of one activeagent overlaps with the physiological effect of a second active agent.

The term “subject,” “patient,” or “individual” typically includeshumans, but also includes other animals such as, e.g., other primates,rodents, canines, felines, equines, ovines, porcines, and the like.

As used herein, the terms “immunosuppressive drug,” “immunosuppressiveagent,” and “immunomodulator” include any substance capable of producingan immunosuppressive effect, e.g., the prevention or diminution of theimmune response, as by irradiation or by administration orco-administration of drugs or agents such as anti-metabolites,anti-folates, thiopurine drugs, anti-lymphocyte sera, antibodies, etc.Non-limiting examples of immunosuppressive drugs include anti-folates(e.g., methotrexate (MTX)), thiopurine drugs (e.g., azathioprine (AZA)),sirolimus (rapamycin), temsirolimus (Torisel®)), everolimus (Afinitor®),tacrolimus (FK-506), FK-778, anti-lymphocyte globulin antibodies,anti-thymocyte globulin antibodies, anti-CD3 antibodies, anti-CD4antibodies, antibody-toxin conjugates, cyclosporine, mycophenolate,mizoribine monophosphate, scoparone, glatiramer acetate,pharmaceutically acceptable salts thereof, metabolites thereof,derivatives thereof, prodrugs thereof, and combinations thereof.

The term “thiopurine drug” includes azathioprine (AZA), 6-mercaptopurine(6-MP), or any metabolite thereof that has therapeutic efficacy andincludes, without limitation, 6-thioguanine (6-TG),6-methylmercaptopurine riboside, 6-thioinosine nucleotides (e.g.,6-thioinosine monophosphate, 6-thioinosine diphosphate, 6-thioinosinetriphosphate), 6-thioguanine nucleotides (e.g., 6-thioguanosinemonophosphate, 6-thioguanosine diphosphate, 6-thioguanosinetriphosphate), 6-thioxanthosine nucleotides (e.g., 6-thioxanthosinemonophosphate, 6-thioxanthosine diphosphate, 6-thioxanthosinetriphosphate), derivatives thereof, analogues thereof, and combinationsthereof.

The term “sample” includes any biological specimen obtained from asubject. Samples include, without limitation, whole blood, plasma,serum, red blood cells, white blood cells (e.g., peripheral bloodmononuclear cells (PBMC), polymorphonuclear (PMN) cells), ductal lavagefluid, nipple aspirate, lymph (e.g., disseminated tumor cells of thelymph node), bone marrow aspirate, saliva, urine, stool (i.e., feces),sputum, bronchial lavage fluid, tears, fine needle aspirate (e.g.,harvested by random periareolar fine needle aspiration), any otherbodily fluid, a tissue sample such as a biopsy of a site of inflammation(e.g., needle biopsy), cellular extracts thereof, and an immunoglobulinenriched fraction derived from one or more of these bodily fluids ortissues. In some embodiments, the sample is whole blood, a fractionalcomponent thereof such as plasma, serum, or a cell pellet, or animmunoglobulin enriched fraction thereof. One skilled in the art willappreciate that samples such as serum samples can be diluted prior tothe analysis. In certain embodiments, the sample is obtained byisolating PBMCs and/or PMN cells using any technique known in the art.In certain other embodiments, the sample is a tissue biopsy such as,e.g., from a site of inflammation such as a portion of thegastrointestinal tract.

In “quartile analysis”, there are three numbers (values) that divide arange of data into four equal parts. The first quartile (also called the‘lower quartile’) is the number below which lies the bottom 25 percentof the data. The second quartile (the ‘median’) divides the range in themiddle and has 50 percent of the data below it. The third quartile (alsocalled the ‘upper quartile’) has 75 percent of the data below it and thetop 25 percent of the data above it. As a non-limiting example, quartileanalysis can be applied to the concentration level of a marker such asan antibody or other protein marker described herein, such that a markerlevel in the first quartile (<25%) is assigned a value of 1, a markerlevel in the second quartile (25-50%) is assigned a value of 2, a markerlevel in the third quartile (51%-<75%) is assigned a value of 3, and amarker level in the fourth quartile (75%-100%) is assigned a value of 4.

As used herein, the phrase “at a later time point” includes phrases suchas “by a later time point” and “within the later time point.” Forexample, a method for predicting whether a subject will developautoantibodies to an anti-TNFα drug at a later time point during acourse of therapy includes a method for predicting whether a subjectwill develop autoantibodies to an anti-TNFα drug by the later time pointduring the course of therapy as well as a method for predicting whethera subject will develop autoantibodies to an anti-TNFα drug within thelater time point during the course of therapy.

The steps of the methods of the present invention do not necessarilyhave to be performed in the particular order in which they arepresented. A person of ordinary skill in the art would understand thatother orderings of the steps of the methods of the invention areencompassed within the scope of the present invention.

III. Description of the Embodiments

In some aspects, the present invention provides methods for predictingwhether a subject will develop autoantibodies to an anti-TNFα drugduring the course of anti-TNFα drug therapy. In other aspects, thepresent invention provides methods for predicting the level of ananti-TNFα drug in a subject during the course of anti-TNFα drug therapy.Systems for predicting anti-TNFα drug levels and the likelihood ofautoantibody formation during the course of anti-TNFα drug therapy arealso provided herein. The present invention further provides methods forpredicting a clinical outcome (e.g., endoscopic response) of a subjecton anti-TNFα drug therapy.

In one aspect, the present invention provides a method for predictingwhether a subject will develop autoantibodies to an anti-TNFα drug at alater time point during a course of therapy with the anti-TNFα drug, themethod comprising measuring the level of the anti-TNFα drug in a sampleobtained from the subject at an earlier time point during the course oftherapy.

In some embodiments, the subject has inflammatory bowel disease (IBD) ora clinical subtype thereof such as Crohn's disease (CD) or ulcerativecolitis (UC). In other embodiments, the sample is a whole blood, serum,or plasma sample.

In some embodiments, the course of therapy is monotherapy with theanti-TNFα drug. In other embodiments, the course of therapy iscombination therapy with the anti-TNFα drug and an immunosuppressiveagent. Non-limiting examples of immunosuppressive agents includeanti-metabolites, e.g., methotrexate (MTX) and other anti-folates,thiopurine drugs such as azathioprine (AZA) and 6-mercaptopurine (6-MP),and combinations thereof.

In certain embodiments, the anti-TNFα drug is selected from the groupconsisting of REMICADE™ (infliximab), ENBREL® (etanercept), HUMIRA®(adalimumab), CIMZIA® (certolizumab pegol), SIMPONI® (golimumab), andcombinations thereof.

In some embodiments, the autoantibodies to the anti-TNFα drug are humananti-chimeric antibodies (HACA), human anti-humanized antibodies (HAHA),human anti-mouse antibodies (HAMA), or combinations thereof.

In some embodiments, the earlier time point is at day 1, 2, 3, 4, 5, 6,7, 8, 9, or 10, or at week 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12during the course of therapy. In certain embodiments, the earlier timepoint is at week 2 or week 6 during the course of therapy. In otherembodiments, the later time point is at week 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 during thecourse of therapy. In certain embodiments, the later time point is atweek 14 during the course of therapy (e.g., by week 14 or within thefirst 14 weeks of therapy). In preferred embodiments, the earlier timepoint is at week 2 or week 6 during the course of therapy, and the latertime point is at week 14 during the course of therapy.

In particular embodiments, the method further comprises comparing themeasured level of the anti-TNFα drug to a reference level of theanti-TNFα drug. In certain instances, a reference level of the anti-TNFαdrug can be established from IBD (e.g., CD) subjects on therapy with thedrug. In some embodiments, the method predicts that the subject will notdevelop autoantibodies to the anti-TNFα drug at a later time pointduring the course of therapy when the measured level of the anti-TNFαdrug is greater than or equal to the reference level of the anti-TNFαdrug. In certain embodiments, the reference level corresponds to a meanlevel or a specific quartile level (e.g., Q1, Q2, Q3, Q4 obtained fromquartile analysis) of the anti-TNFα drug from a dataset of samples fromIBD (e.g., CD) subjects on therapy with the drug. For example, thereference level of the anti-TNFα drug can be the Q4 value from a datasetof a plurality of anti-TNFα drug assays using samples from IBD (e.g.,CD) subjects on therapy with the drug. In preferred embodiments, thereference level is derived from quartile analysis of a referencedatabase of samples from IBD (e.g., CD) subjects on anti-TNFα drugtherapy and corresponds to the level of the anti-TNFα drug in thequartile that contains samples with the highest anti-TNFα drug levels(e.g., Q4).

As a non-limiting example, a subject is predicted not to developautoantibodies to infliximab (ATI) at week 14 if the infliximab level atweek 2 is greater than a reference level of about 37 μg/ml (i.e., the Q4value). As another non-limiting example, a subject is predicted not todevelop ATI at week 14 if the infliximab level at week 6 is greater thana reference level of about 35 μg/ml (i.e., the Q4 value). As yet anothernon-limiting example, a subject is predicted not to develop ATI at week14 if the infliximab level at week 14 is greater than a reference levelof about 14 μg/ml (i.e., the Q4 value).

In another aspect, the present invention provides a method forpredicting the level of an anti-TNFα drug in a subject at a later timepoint during a course of therapy with the anti-TNFα drug, the methodcomprising determining one or more predictor variables for the subjectat an earlier time point during the course of therapy and/or prior tothe initiation of the course of therapy.

In some embodiments, the subject has inflammatory bowel disease (IBD) ora clinical subtype thereof such as Crohn's disease (CD) or ulcerativecolitis (UC).

In some embodiments, the course of therapy is monotherapy with theanti-TNFα drug. In other embodiments, the course of therapy iscombination therapy with the anti-TNFαdrug and an immunosuppressiveagent. Non-limiting examples of immunosuppressive agents includeanti-metabolites, e.g., methotrexate (MTX) and other anti-folates,thiopurine drugs such as azathioprine (AZA) and 6-mercaptopurine (6-MP),and combinations thereof.

In certain embodiments, the anti-TNFα drug is selected from the groupconsisting of REMICADE™ (infliximab), ENBREL® (etanercept), HUMIRA®(adalimumab), CIMZIA® (certolizumab pegol), SIMPONI® (golimumab), andcombinations thereof.

In some embodiments, the one or more predictor variables comprises atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, or more predictor variables. In certain embodiments, the one or morepredictor variables is selected from the group consisting of TNFα level,anti-TNFα drug level, C-reactive protein (CRP) level, human serumalbumin (HSA) level, immunomodulator (IMM) use, gender, age, age atdiagnosis, Body Mass Index (BMI) at first drug dose, hemoglobin (Hb)level at first drug dose, age at first drug dose (years), surgeryprevious to first drug dose, ratio of TNFα level to drug level, presenceof autoantibodies to the drug, and combinations thereof.

In certain instances, the one or more predictor variables is determinedprior to the initiation of the course of therapy. In certain otherinstances, the one or more predictor variables is determined prior tothe initiation of the course of therapy and at one or more times duringthe course of therapy.

In some embodiments, the method comprises determining the one or morepredictor variables prior to the initiation of the course of therapy(i.e., baseline values) to predict the level of the anti-TNFα drug at alater time point during the course of therapy. As a non-limitingexample, baseline values (i.e., at week 0) of a combination of thepredictor variables TNFα (e.g., Log [TNFα]), albumin, age, and BMI aredetermined to predict the level of infliximab (IFX) at a later timeduring the course of therapy (e.g., at week 14). As another non-limitingexample, baseline values (i.e., at week 0) of a combination of thepredictor variables CRP (e.g., Log [CRP]), albumin, gender, and BMI aredetermined to predict the level of IFX at a later time during the courseof therapy (e.g., at week 2).

In other embodiments, the method comprises determining the one or morepredictor variables prior to the initiation of the course of therapy andat one or more times during the course of therapy to predict the levelof the anti-TNFα drug at a later time point during the course oftherapy. As a non-limiting example, baseline values (i.e., at week 0) ofa combination of the predictor variables IMM use during IFX inductionand previous surgery and week 2 values of IFX (e.g., Log [IFX]) and CRP(e.g., Log [CRP]) are determined to predict the level of IFX at a latertime during the course of therapy (e.g., at week 6). As anothernon-limiting example, baseline values (i.e., at week 0) of the predictorvariable age at 1st IFX (years), week 2 values of the predictor variableIFX (e.g., Log [IFX]), and week 6 values of a combination of thepredictor variables IFX (e.g., Log [IFX]), total ATI, and CRP (e.g., Log[CRP]) are determined to predict the level of IFX at a later time duringthe course of therapy (e.g., at week 14). As yet another non-limitingexample, baseline values (i.e., at week 0) of the predictor variable ageat 1st IFX (years), week 2 values of the predictor variable IFX (e.g.,Log [IFX]), and week 6 values of a combination of the predictorvariables IFX (e.g., Log [IFX]), total ATI, TNFα (e.g., Log [TNFα]), andCRP (e.g., Log [CRP]) are determined to predict the level of IFX at alater time during the course of therapy (e.g., at week 14).

In some embodiments, the earlier time point or a plurality of one ormore time points is at day 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or at week1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, or any combination thereof,during the course of therapy. In certain embodiments, the earlier timepoint is at week 2 or week 6 during the course of therapy. In otherembodiments, the plurality of one or more time points is at week 2 andweek 6 during the course of therapy. In yet other embodiments, the latertime point is at week 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 during the course of therapy.In certain embodiments, the later time point is at week 2, week 6, orweek 14 during the course of therapy. In preferred embodiments, theearlier time point is at week 2 or week 6 or a combination thereofduring the course of therapy, and the later time point is at week 14during the course of therapy.

In other embodiments, the method further comprises applying astatistical analysis on the one or more predictor variables. Inparticular embodiments, the statistical analysis comprises a multiplelogistic regression model. In certain embodiments, an initial dose ofthe anti-TNFα drug is determined based upon the statistical analysis.

In yet another aspect, the present invention provides a method forpredicting whether a subject will develop autoantibodies to an anti-TNFαdrug at a later time point during a course of therapy with the anti-TNFαdrug, the method comprising determining one or more predictor variablesfor the subject at an earlier time point during the course of therapyand/or prior to the initiation of the course of therapy.

In some embodiments, the subject has inflammatory bowel disease (IBD) ora clinical subtype thereof such as Crohn's disease (CD) or ulcerativecolitis (UC).

In some embodiments, the course of therapy is monotherapy with theanti-TNFα drug. In other embodiments, the course of therapy iscombination therapy with the anti-TNFα drug and an immunosuppressiveagent. Non-limiting examples of immunosuppressive agents includeanti-metabolites, e.g., methotrexate (MTX) and other anti-folates,thiopurine drugs such as azathioprine (AZA) and 6-mercaptopurine (6-MP),and combinations thereof.

In certain embodiments, the anti-TNFα drug is selected from the groupconsisting of REMICADE™ (infliximab), ENBREL® (etanercept), HUMIRA®(adalimumab), CIMZIA® (certolizumab pegol), SIMPONI® (golimumab), andcombinations thereof.

In some embodiments, the autoantibodies to the anti-TNFα drug are humananti-chimeric antibodies (HACA), human anti-humanized antibodies (HAHA),human anti-mouse antibodies (HAMA), or combinations thereof.

In some embodiments, the one or more predictor variables comprises atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, or more predictor variables. In certain embodiments, the one or morepredictor variables is selected from the group consisting of TNFα level,anti-TNFα drug level, C-reactive protein (CRP) level, human serumalbumin (HSA) level, immunomodulator (IMM) use, gender, age, age atdiagnosis, Body Mass Index (BMI) at first drug dose, hemoglobin (Hb)level at first drug dose, age at first drug dose (years), surgeryprevious to first drug dose, ratio of TNFα level to drug level, presenceof autoantibodies to the drug, and combinations thereof.

In certain instances, the one or more predictor variables is determinedprior to the initiation of the course of therapy. In certain otherinstances, the one or more predictor variables is determined prior tothe initiation of the course of therapy and at one or more times duringthe course of therapy.

In some embodiments, the method comprises determining the one or morepredictor variables prior to the initiation of the course of therapy(i.e., baseline values) to predict whether the subject will developautoantibodies to the anti-TNFα drug at a later time point during thecourse of therapy (e.g., by the later time point or within the latertime point during therapy). As a non-limiting example, baseline values(i.e., at week 0) of a combination of the predictor variables TNFα(i.e., Log [TNFα]), gender, hemoglobin at 1st IFX, and IMM use duringIFX induction are determined to predict ATI formation at a later timeduring the course of therapy (e.g., at week 14, by week 14, or withinthe first 14 weeks of therapy).

In other embodiments, the method comprises determining the one or morepredictor variables prior to the initiation of the course of therapy andat one or more times during the course of therapy to predict whether thesubject will develop autoantibodies to the anti-TNFα drug at a latertime point during the course of therapy (e.g., by the later time pointor within the later time point during therapy). As a non-limitingexample, baseline values (i.e., at week 0) of a combination of thepredictor variables TNFα (e.g., Log [TNFα]), IMM use during IFXinduction, gender, and hemoglobin at 1st IFX, and week 2 values of acombination of the predictor variables albumin and IFX (e.g., Log [IFX])are determined to predict ATI formation at a later time during thecourse of therapy (e.g., at week 14, by week 14, or within the first 14weeks of therapy). As another non-limiting example, baseline values(i.e., at week 0) of a combination of the predictor variables TNFα(e.g., Log [TNFα]), gender, and hemoglobin at 1st IFX, and week 6 valuesof a combination of the predictor variables albumin and IFX (e.g., Log[IFX]) are determined to predict ATI formation at a later time duringthe course of therapy (e.g., at week 14, by week 14, or within the first14 weeks of therapy). As yet another non-limiting example, baselinevalues (i.e., at week 0) of a combination of the predictor variables CRP(e.g., Log [CRP]), gender, and hemoglobin at 1st IFX, week 2 values ofthe predictor variable TNFα (e.g., Log [TNFα]), week 6 values of thepredictor variable albumin, and week 14 values of a combination of thepredictor variables TNFα (e.g., Log [TNFα]), IFX (e.g., Log [IFX]), andCRP (e.g., Log [CRP]) are determined to predict ATI formation at a latertime during the course of therapy (e.g., at week 14, by week 14, orwithin the first 14 weeks of therapy). As another non-limiting example,baseline values (i.e., at week 0) of a combination of the predictorvariables CRP (e.g., Log [CRP]), gender, and hemoglobin at 1st IFX, week2 values of the predictor variable TNFα (e.g., Log [TNFα]), week 6values of the predictor variable albumin, and week 14 values of acombination of the predictor variables TNFα/IFX ratio (e.g., Log[TNFα/IFX]) and CRP (e.g., Log [CRP]) are determined to predict ATIformation at a later time during the course of therapy (e.g., at week14, by week 14, or within the first 14 weeks of therapy).

In some embodiments, the earlier time point or a plurality of one ormore time points is at day 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or at week1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, or any combination thereof,during the course of therapy. In certain embodiments, the earlier timepoint is at week 2 or week 6 during the course of therapy. In otherembodiments, the plurality of one or more time points is at week 2 andweek 6 during the course of therapy. In yet other embodiments, the latertime point is at week 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 during the course of therapy.In certain embodiments, the later time point is at week 2, week 6, orweek 14 during the course of therapy (e.g., by week 2, week 6, or week14 or within the first 2 weeks, 6 weeks, or 14 weeks of therapy). Inpreferred embodiments, the earlier time point is at week 2, week 6, week14, or a combination thereof during the course of therapy, and the latertime point is at week 14 during the course of therapy (e.g., by week 14or within the first or 14 weeks of therapy).

In other embodiments, the method further comprises applying astatistical analysis on the one or more predictor variables. Inparticular embodiments, the statistical analysis comprises a multiplelogistic regression model. In certain embodiments, an initial dose ofthe anti-TNFα drug is determined based upon the statistical analysis. Insome instances, the initial dose of the anti-TNFα drug is increasedrelative to a normal starting dose of the anti-TNFα drug if the subjectis predicted to develop autoantibodies to the anti-TNFα drug. In otherinstances, the initial dose of the anti-′TNFα drug further comprises anincreased dose of an immunosuppressive agent.

In an additional aspect, the present invention provides a system forpredicting the level of an anti-TNFα drug in a subject at a later timepoint during a course of therapy with the anti-TNFα drug, the systemcomprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising one or more predictor variables for the subject        determined at an earlier time point during the course of therapy        and/or prior to the initiation of the course of therapy;    -   (b) a data processing module configured to process the data set        by applying a statistical analysis to the data set to produce a        statistically derived decision predicting the level of the        anti-TNFα drug based upon the one or more predictor variables;        and    -   (c) a display module configured to display the statistically        derived decision.

In a further aspect, the present invention provides a system forpredicting whether a subject will develop autoantibodies to an anti-TNFαdrug at a later time point during a course of therapy with the anti-TNFαdrug, the system comprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising one or more predictor variables for the subject        determined at an earlier time point during the course of therapy        and/or prior to the initiation of the course of therapy;    -   (b) a data processing module configured to process the data set        by applying a statistical analysis to the data set to produce a        statistically derived decision predicting whether the subject        will develop autoantibodies to the anti-TNFα drug based upon the        one or more predictor variables; and    -   (c) a display module configured to display the statistically        derived decision.

In some embodiments, the subject has inflammatory bowel disease (IBD) ora clinical subtype thereof such as Crohn's disease (CD) or ulcerativecolitis (UC).

In some embodiments, the course of therapy is monotherapy with theanti-TNFα drug. In other embodiments, the course of therapy iscombination therapy with the anti-TNFα drug and an immunosuppressiveagent. Non-limiting examples of immunosuppressive agents includeanti-metabolites, e.g., methotrexate (MTX) and other anti-folates,thiopurine drugs such as azathioprine (AZA) and 6-mercaptopurine (6-MP),and combinations thereof.

In certain embodiments, the anti-TNFα drug is selected from the groupconsisting of REMICADE™ (infliximab), ENBREL® (etanercept), HUMIRA®(adalimumab), CIMZIA® (certolizumab pegol), SIMPONI® (golimumab), andcombinations thereof.

In some embodiments, the autoantibodies to the anti-TNFα drug are humananti-chimeric antibodies (HACA), human anti-humanized antibodies (HAHA),human anti-mouse antibodies (HAMA), or combinations thereof.

In some embodiments, the one or more predictor variables comprises atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, or more predictor variables. In certain embodiments, the one or morepredictor variables is selected from the group consisting of TNFα level,C-reactive protein (CRP) level, human serum albumin (HSA) level,immunomodulator (IMM) use, gender, age, age at diagnosis, Body MassIndex (BMI) at first drug dose, hemoglobin (Hb) level at first drugdose, age at first drug dose (years), surgery previous to first drugdose, ratio of TNFα level to drug level, presence of autoantibodies tothe drug, and combinations thereof.

In certain instances, the one or more predictor variables is determinedprior to the initiation of the course of therapy. In certain otherinstances, the one or more predictor variables is determined prior tothe initiation of the course of therapy and at one or more times duringthe course of therapy.

In some embodiments, the earlier time point or a plurality of one ormore time points is at day 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or at week1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, or any combination thereof,during the course of therapy. In certain embodiments, the earlier timepoint is at week 2 or week 6 during the course of therapy. In otherembodiments, the plurality of one or more time points is at week 2 andweek 6 during the course of therapy. In yet other embodiments, the latertime point is at week 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 during the course of therapy.In certain embodiments, the later time point is at week 2, week 6, orweek 14 during the course of therapy (e.g., by week 2, week 6, or week14 or within the first 2 weeks, 6 weeks, or 14 weeks of therapy). Inpreferred embodiments, the earlier time point is at week 2, week 6, week14, or a combination thereof during the course of therapy, and the latertime point is at week 14 during the course of therapy (e.g., by week 14or within the first or 14 weeks of therapy).

In particular embodiments, the statistical analysis comprises a multiplelogistic regression model. In certain embodiments, an initial dose ofthe anti-TNFα drug is determined based upon the statistically deriveddecision.

In another aspect, the present invention provides a method forpredicting a clinical outcome of a subject at a later time point duringa course of therapy with the anti-TNFα drug, the method comprisingdetermining one or more predictor variables selected from the level ofIL12p40, the level of IL-8, the level of the anti-TNFα drug, andcombinations thereof in a sample obtained from the subject at an earliertime point during the course of therapy.

In some embodiments, the subject has inflammatory bowel disease (IBD) ora clinical subtype thereof such as Crohn's disease (CD) or ulcerativecolitis (UC). In other embodiments, the sample is a whole blood, serum,or plasma sample.

In some embodiments, the course of therapy is monotherapy with theanti-TNFα drug. In other embodiments, the course of therapy iscombination therapy with the anti-TNFα drug and an immunosuppressiveagent. Non-limiting examples of immunosuppressive agents includeanti-metabolites, e.g., methotrexate (MTX) and other anti-folates,thiopurine drugs such as azathioprine (AZA) and 6-mercaptopurine (6-MP),and combinations thereof.

In certain embodiments, the anti-TNFα drug is selected from the groupconsisting of REMICADE™ (infliximab), ENBREL® (etanercept), HUMIRA®(adalimumab), CIMZIA® (certolizumab pegol), SIMPONI® (golimumab), andcombinations thereof.

In certain embodiments, the clinical outcome corresponds to anendoscopic response at week 8 during the course of therapy. In otherembodiments, the method comprises measuring the level of IL12p40 and thelevel of IL-8 in the sample.

In some embodiments, the earlier time point is at day 1, 2, 3, 4, 5, 6,7, 8, 9, or 10, or at week 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12during the course of therapy. In certain embodiments, the earlier timepoint is at 24 hours after dosing or at week 2 during the course oftherapy. In other embodiments, the later time point is at week 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, or 25 during the course of therapy. In certain embodiments, thelater time point is at week 8 during the course of therapy (e.g., byweek 8 or within the first 8 weeks of therapy). In preferredembodiments, the earlier time point is at 24 hours after dosing or atweek 2 during the course of therapy, and the later time point is at week8 during the course of therapy.

In particular embodiments, the method further comprises comparing themeasured level of the one or more predictor variables to a referencelevel of the one or more predictor variables. In certain instances, areference level of the one or more predictor variables can beestablished from IBD (e.g., UC) subjects on therapy with the drug whohave responded to the drug (i.e., “responders”). In some embodiments,the method predicts that the subject will or will not have an endoscopicresponse at a later time point during the course of therapy when themeasured level of the predictor variable is less than, greater than, orequal to the reference level of the predictor variable.

As a non-limiting example, a subject is predicted not to have anendoscopic response at week 8 if the level of IL12p40 at week 2 isgreater than a reference level of IL12p40 (e.g., the level of IL12p40 ina sample from a responder at week 2). As another non-limiting example, asubject is predicted not to have an endoscopic response at week 8 if thelevel of IL-8 at week 2 is greater than a reference level of IL12p40(e.g., the level of IL12p40 in a sample from a responder at week 2). Asyet another non-limiting example, a subject is predicted not to have anendoscopic response at week 8 if the level of anti-TNFα drug (e.g., IFX)at 24 hours after dosing is lower than a reference level of theanti-TNFα drug (e.g., the level of the anti-TNFα drug in a sample from aresponder at 24 hours after dosing).

In other embodiments, the method further comprises applying astatistical analysis on the one or more predictor variables. Inparticular embodiments, the statistical analysis comprises a multiplelogistic regression model. In certain embodiments, a clinical outcome atthe later time point is predicted based upon the statistical analysis.

In yet another aspect, the present invention provides a method forpredicting whether a subject will develop autoantibodies to an anti-TNFαdrug at a later time point during a course of therapy with the anti-TNFαdrug, the method comprising determining one or more predictor variablesselected from the level of IL-8, the level of the anti-TNFα drug, theTNFα/drug ratio, and combinations thereof in a sample obtained from thesubject at an earlier time point during the course of therapy.

In some embodiments, the subject has inflammatory bowel disease (IBD) ora clinical subtype thereof such as Crohn's disease (CD) or ulcerativecolitis (UC). In other embodiments, the sample is a whole blood, serum,or plasma sample.

In some embodiments, the course of therapy is monotherapy with theanti-TNFα drug. In other embodiments, the course of therapy iscombination therapy with the anti-TNFα drug and an immunosuppressiveagent. Non-limiting examples of immunosuppressive agents includeanti-metabolites, e.g., methotrexate (MTX) and other anti-folates,thiopurine drugs such as azathioprine (AZA) and 6-mercaptopurine (6-MP),and combinations thereof.

In certain embodiments, the anti-TNFα drug is selected from the groupconsisting of REMICADE™ (infliximab), ENBREL® (etanercept), HUMIRA®(adalimumab), CIMZIA® (certolizumab pegol), SIMPONI® (golimumab), andcombinations thereof.

In some embodiments, the autoantibodies to the anti-TNFα drug are humananti-chimeric antibodies (HACA), human anti-humanized antibodies (HAHA),human anti-mouse antibodies (HAMA), or combinations thereof.

In certain embodiments, the method comprises measuring the level of IL-8and the level of the anti-TNFα drug in the sample. In other embodiments,the method comprises measuring the level of IL-8 and the TNFα/drug ratioin the sample.

In some embodiments, the earlier time point is at day 1, 2, 3, 4, 5, 6,7, 8, 9, or 10, or at week 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12during the course of therapy. In certain embodiments, the earlier timepoint is at 24 hours after dosing or at week 2 during the course oftherapy. In other embodiments, the later time point is at week 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, or 25 during the course of therapy. In certain embodiments, thelater time point is at week 6 during the course of therapy (e.g., byweek 6 or within the first 6 weeks of therapy). In preferredembodiments, the earlier time point is at 24 hours after dosing or atweek 2 during the course of therapy, and the later time point is at week6 during the course of therapy.

In particular embodiments, the method further comprises comparing themeasured level of the one or more predictor variables to a referencelevel of the one or more predictor variables. In certain instances, areference level of the one or more predictor variables can beestablished from IBD (e.g., UC) subjects on therapy with the drug who donot have detectable levels of autoantibodies (i.e., “not detectable”).In some embodiments, the method predicts that the subject will or willnot develop autoantibodies to the anti-′TNFα drug at a later time pointduring the course of therapy when the measured level of the predictorvariable is less than, greater than, or equal to the reference level ofthe predictor variable.

As a non-limiting example, a subject is predicted to developautoantibodies to infliximab (ATI) at week 6 if the level of IL-8 atweek 2 is greater than a reference level of IL-8 (e.g., the level ofIL-8 in a “not detectable” sample at week 2). As another non-limitingexample, a subject is predicted to develop autoantibodies to infliximab(ATI) at week 6 if the level of anti-TNFα drug (e.g., IFX) at 24 hoursafter dosing is lower than a reference level of the anti-TNFα drug(e.g., the level of the anti-TNFα drug in a “not detectable” sample at24 hours after dosing). As yet another non-limiting example, a subjectis predicted to develop autoantibodies to infliximab (ATI) at week 6 ifthe TNFα/drug ratio at 24 hours after dosing is higher than a referenceTNFα/drug ratio (e.g., the TNFα/drug ratio in a “not detectable” sampleat 24 hours after dosing).

In other embodiments, the method further comprises applying astatistical analysis on the one or more predictor variables. Inparticular embodiments, the statistical analysis comprises a multiplelogistic regression model. In certain embodiments, the statisticalanalysis predicts whether the subject will develop autoantibodies to theanti-TNFα drug at a later time point during the course of therapy.

As such, the methods and systems of the present invention advantageouslyenable a clinician to practice “personalized medicine” by guidingpatient selection and prediction with respect to treatment decisions andinforming therapy selection and optimization such that the rightanti-TNFα drug is given to the right patient at the right time.

IV. Measuring TNFα, Anti-TNFα Drug, and Anti-Drug Antibody (ADA) Levels

In some embodiments, the presence and/or level of TNFα is detected,determined, or measured with a CEER™ (Collaborative Enzyme EnhancedReactive) immunoassay. In CEER™ assays, an antibody-microarray basedplatform is utilized to form a unique“triple-antibody-enzyme-channeling” immuno-complex capable of measuringanalytes of limited availability in a sample. For instance, a CEER™assay using an anti-TNFα drug (e.g., infliximab (IFX), etanercept,adalimumab (ADL), certolizumab pegol, or golimumab) as a captureantibody can detect TNFα in serum at levels in the pg/mL range (e.g.,about 0.1 pg/mL or more). The assay can have a sensitivity of less thanabout 0.2 pg/mL. The assays described can determine an analyte to lessthan 50 pg/mL, less than 25 pg/mL, less than 20 pg/mL, less than 10pg/mL, less than 5 pg/mL, less 1 pg/mL or even less. A detaileddescription of CEER™ is found in, e.g., U.S. Pat. No. 8,163,499, whichis hereby incorporated by reference in its entity for all purposes.CEER™ is also described in the following patent documents which areherein incorporated by reference in their entirety for all purposes:International Patent Publication Nos. WO 2008/036802, WO 2009/012140, WO2009/108637, WO 2010/132723, WO 2011/008990, WO 2011/050069; WO2012/088337; WO 2012/119113; and WO 2013/033623.

In other embodiments, an immunoassay such as a sandwich assay or ELISAcan be used to measure TNFα. Non-limiting examples include Human TNF-αHigh Sensitivity ELISA (Cat. No. BMS223HS, eBioscience, San Diego,Calif.), Erenna Human TNFα immunoassay (Cat. No. 03-0022-xx, Singulex,Alameda, Calif.), Human TNFα cytokine assay (Cat. No. K151BHA-5, MesoScale Diagnostics (MSD), Rockville, Md.)) and a multi-marker immunoassay(e.g., as described in U.S. Pat. No. 8,450,069; Singulex). The assaysdescribed can determine an analyte to less than 50 pg/mL, less than 25pg/mL, less than 20 pg/mL, less than 10 pg/mL, less than 5 pg/mL, less 1pg/mL or even less.

In some embodiments, the presence and/or level of an anti-TNFα drugand/or ADA (e.g., ATI formation) is detected, determined, or measuredwith a homogeneous mobility shift assay (HMSA) using size exclusionchromatography. These methods are described in U.S. Pat. No. 8,574,855;U.S. Patent Publication Nos. 2012/0329172 and 2014/0051184; and PCTPublication. No. WO2012/154987, the disclosures of which are herebyincorporated by reference in their entirety for all purposes. Themethods are particularly useful for measuring the presence or level ofTNFα inhibitors as well as autoantibodies (e.g., HACA, HAHA, etc.) thatare generated against them.

V. Statistical Analysis

In certain aspects, the present invention provides models to predict thelevel of an anti-TNFα drug, the clinical outcome on anti-TNFα drugtherapy, and/or the likelihood of developing anti-drug antibodies. Inparticular embodiments, the model is an algorithmic model which uses oneor more predictor variables including TNFα level, anti-TNFα drug level,C-reactive protein (CRP) level, human serum albumin (HSA) level,immunomodulator (IMM) use, gender, age, age at diagnosis, Body MassIndex (BMI) at first drug dose, hemoglobin (Hb) level at first drugdose, age at first drug dose (years), surgery previous to first drugdose, ratio of TNFα level to drug level, presence of autoantibodies tothe drug, IL12p40 level, IL-8 level, and combinations thereof.

An algorithmic model includes any of a variety of statistical methodsand models used to determine relationships between variables. In thepresent invention, the variables are the values of the one or morepredictor variables at an earlier time point during the course ofanti-TNFα drug therapy (e.g., at week 2, 6, and/or 14) and/or prior tothe initiation of the course of therapy (i.e., baseline or week 0). Anynumber of predictor variables can be analyzed using a statisticalanalysis described herein. For example, the value of 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45,50, 55, 60, or more predictor variables can be included in a statisticalanalysis such as, e.g., a multiple logistic regression model.

In particular embodiments, quantile analysis is applied to the value ofone or more predictor variables to guide treatment decisions forpatients receiving anti-TNFα drug therapy. In other embodiments, one ora combination of two of more statistical algorithms such as learningstatistical classifier systems are applied to the value of one or morepredictor variables to guide treatment decisions for patients receivinganti-TNFα drug therapy. The statistical analyses of the methods of thepresent invention advantageously assist in determining the initial dosean anti-TNFα drug, when or how to adjust or modify (e.g., increase ordecrease) the subsequent dose of an anti-TNFα drug, to combine ananti-TNFα drug (e.g., at an increased, decreased, or same dose) with oneor more immunosuppressive agents such as methotrexate (MTX) orazathioprine (AZA), and/or to change the current course of therapy(e.g., switch to a different anti-TNF drug).

The algorithmic model includes the value of one or more predictorvariables along with a statistical algorithm such as a multiple logisticregression analysis. In certain instances, the model has been trainedwith known outcomes using a training set cohort of samples. Thealgorithm is then validated using a validation cohort. Patient unknownsamples can then be predicted based on the trained algorithms.

The term “statistical analysis” or “statistical algorithm” or“statistical process” includes any of a variety of statistical methodsand models used to determine relationships between variables. In thepresent invention, the variables are the values or measurements of theone or more predictor variables described herein. Any number ofpredictor variables can be analyzed using a statistical analysisdescribed herein. In preferred embodiments, logistic regression is used(e.g., a multiple logistic regression model). In other embodiments,linear regression is used. In further embodiments, a Cox proportionalhazards regression model is used.

In certain embodiments, the statistical analysis of the presentinvention comprises a quantile measurement of one or more predictorvariables (e.g., markers such as anti-TNFα drug levels) within a givenpopulation. Quantiles are a set of “cut points” that divide a sample ofdata into groups containing (as far as possible) equal numbers ofobservations. For example, quartiles are values that divide a sample ofdata into four groups containing (as far as possible) equal numbers ofobservations. The lower quartile is the data value a quarter way upthrough the ordered data set; the upper quartile is the data value aquarter way down through the ordered data set. Quintiles are values thatdivide a sample of data into five groups containing (as far as possible)equal numbers of observations. The present invention can also includethe use of percentile ranges of marker levels (e.g., tertiles, quartile,quintiles, etc.), or their cumulative indices (e.g., quartile sums ofmarker levels to obtain quartile sum scores (QSS), etc.) as variables inthe statistical analyses (just as with continuous variables).

In particular embodiments, the statistical analysis comprises one ormore learning statistical classifier systems. As used herein, the term“learning statistical classifier system” includes a machine learningalgorithmic technique capable of adapting to complex data sets (e.g.,panel of predictor variables) and making decisions based upon such datasets. In some embodiments, a single learning statistical classifiersystem such as a decision/classification tree (e.g., random forest (RF)or classification and regression tree (C&RT)) is used. In otherembodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or morelearning statistical classifier systems are used, preferably in tandem.Examples of learning statistical classifier systems include, but are notlimited to, those using inductive learning (e.g.,decision/classification trees such as random forests, classification andregression trees (C&RT), boosted trees, etc.), Probably ApproximatelyCorrect (PAC) learning, connectionist learning (e.g., neural networks(NN), artificial neural networks (ANN), neuro fuzzy networks (NFN),network structures, perceptrons such as multi-layer perceptrons,multi-layer feed-forward networks, applications of neural networks,Bayesian learning in belief networks, etc.), reinforcement learning(e.g., passive learning in a known environment such as naïve learning,adaptive dynamic learning, and temporal difference learning, passivelearning in an unknown environment, active learning in an unknownenvironment, learning action-value functions, applications ofreinforcement learning, etc.), and genetic algorithms and evolutionaryprogramming. Other learning statistical classifier systems includesupport vector machines (e.g., Kernel methods), multivariate adaptiveregression splines (MARS), Levenberg-Marquardt algorithms, Gauss-Newtonalgorithms, mixtures of Gaussians, gradient descent algorithms, andlearning vector quantization (LVQ).

Random forests are learning statistical classifier systems that areconstructed using an algorithm developed by Leo Breiman and AdeleCutler. Random forests use a large number of individual decision treesand decide the class by choosing the mode (i.e., most frequentlyoccurring) of the classes as determined by the individual trees. Randomforest analysis can be performed, e.g., using the RandomForests softwareavailable from Salford Systems (San Diego, Calif.). See, e.g., Breiman,Machine Learning, 45:5-32 (2001); andhttp://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm,for a description of random forests.

Classification and regression trees represent a computer intensivealternative to fitting classical regression models and are typicallyused to determine the best possible model for a categorical orcontinuous response of interest based upon one or more predictors.Classification and regression tree analysis can be performed, e.g.,using the C&RT software available from Salford Systems or the Statisticadata analysis software available from StatSoft, Inc. (Tulsa, Okla.). Adescription of classification and regression trees is found, e.g., inBreiman et al. “Classification and Regression Trees,” Chapman and Hall,New York (1984); and Steinberg et al., “CART: Tree-StructuredNon-Parametric Data Analysis,” Salford Systems, San Diego, (1995).

Neural networks are interconnected groups of artificial neurons that usea mathematical or computational model for information processing basedon a connectionist approach to computation. Typically, neural networksare adaptive systems that change their structure based on external orinternal information that flows through the network. Specific examplesof neural networks include feed-forward neural networks such asperceptrons, single-layer perceptrons, multi-layer perceptrons,backpropagation networks, ADALINE networks, MADALINE networks,Learnmatrix networks, radial basis function (RBF) networks, andself-organizing maps or Kohonen self-organizing networks; recurrentneural networks such as simple recurrent networks and Hopfield networks;stochastic neural networks such as Boltzmann machines; modular neuralnetworks such as committee of machines and associative neural networks;and other types of networks such as instantaneously trained neuralnetworks, spiking neural networks, dynamic neural networks, andcascading neural networks. Neural network analysis can be performed,e.g., using the Statistica data analysis software available fromStatSoft, Inc. See, e.g., Freeman et al., In “Neural Networks:Algorithms, Applications and Programming Techniques,” Addison-WesleyPublishing Company (1991); Zadeh, Information and Control, 8:338-353(1965); Zadeh, “IEEE Trans. on Systems, Man and Cybernetics,” 3:28-44(1973); Gersho et al., In “Vector Quantization and Signal Compression,”Kluywer Academic Publishers, Boston, Dordrecht, London (1992); andHassoun, “Fundamentals of Artificial Neural Networks,” MIT Press,Cambridge, Mass., London (1995), for a description of neural networks.

Support vector machines are a set of related supervised learningtechniques used for classification and regression and are described,e.g., in Cristianini et al., “An Introduction to Support Vector Machinesand Other Kernel-Based Learning Methods,” Cambridge University Press(2000). Support vector machine analysis can be performed, e.g., usingthe SVM^(light) software developed by Thorsten Joachims (CornellUniversity) or using the LIB SVM software developed by Chih-Chung Changand Chih-Jen Lin (National Taiwan University).

The various statistical methods and models described herein can betrained and tested using a cohort of samples (e.g., serological samples)from healthy individuals, patients with the disease or disorder ofinterest (e.g., IBD patients such as CD and/or UC patients), and/orpatients on therapy (e.g., anti-TNFα drug therapy). For example, samplesfrom patients diagnosed by a physician, and preferably by agastroenterologist, as having IBD or a clinical subtype thereof using abiopsy, colonoscopy, or an immunoassay as described in, e.g., U.S. Pat.No. 6,218,129, are suitable for use in training and testing thestatistical methods and models of the present invention. Samples frompatients diagnosed with IBD can also be stratified into Crohn's diseaseor ulcerative colitis using an immunoassay as described in, e.g., U.S.Pat. Nos. 5,750,355 and 5,830,675. Samples from healthy individuals caninclude those that were not identified as IBD samples. One skilled inthe art will know of additional techniques and diagnostic criteria forobtaining a cohort of patient samples that can be used in training andtesting the statistical methods and models of the present invention.

The statistical methods and models described herein can be selected suchthat the sensitivity is at least about 60%, and can be, e.g., at leastabout 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The statistical methods and models described herein can be selected suchthat the specificity is at least about 60%, and can be, e.g., at leastabout 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The statistical methods and models described herein can be selected suchthat the negative predictive value in a population having a diseaseprevalence is in the range of about 70% to about 99% and can be, forexample, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99%.

The statistical methods and models described herein can be selected suchthat the positive predictive value in a population having a diseaseprevalence is in the range of about 70% to about 99% and can be, forexample, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99%.

The statistical methods and models described herein can be selected fora disease prevalence of up to about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%,10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, or 70%,which can be seen, e.g., in a clinician's office such as agastroenterologist's office or a general practitioner's office.

The statistical methods and models described herein can be selected suchthat the overall accuracy is at least about 40%, and can be, e.g., atleast about 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%,52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%,66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%,80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, 98%, or 99%.

In certain embodiments, the statistical analysis comprises calculatingor applying a hazard ratio (HR). In certain instances, the HR iscalculated using a Cox Proportional Hazard Model. The Cox regressionmodel provides an estimate of the hazard ratio and its confidenceinterval. The confidence interval provides an estimate of the precisionof the HR. A large confidence interval indicates a lower HR precision,while a small confidence interval indicates an HR with a high precision.A p-value indicates whether the HR is statistically significant. In someembodiments, the hazard is the formation of anti-drug antibodies and theHR is the multiplicative effect on the hazard.

VI. Predictive Models and Systems

In some aspects, the present invention provides a system for predictingthe level of an anti-TNFα drug in a subject at a later time point duringa course of therapy with the anti-TNFα drug. In other aspects, thepresent invention provides a system for predicting whether a subjectwill develop autoantibodies to an anti-TNFα drug at a later time pointduring a course of therapy with the anti-′TNFα drug. In yet otheraspects, the present invention provides a system for predicting aclinical outcome of a subject at a later time point during a course oftherapy with the anti-TNFα drug.

In certain embodiments, the system comprises: a data acquisition moduleconfigured to produce a data set comprising one or more predictorvariables for the subject determined at an earlier time point during thecourse of therapy and/or prior to the initiation of the course oftherapy; a data processing module configured to process the data set byapplying a statistical analysis to the data set to produce astatistically derived decision predicting the level of the anti-TNFαdrug or predicting whether the subject will develop autoantibodies tothe anti-TNFα drug or predicting a clinical outcome of the subjectreceiving the anti-TNFα drug based upon the one or more predictorvariables; and a display module configured to display the statisticallyderived decision.

In some embodiments, the system includes an intelligence module, such asa computer, having a processor and memory module. The intelligencemodule may also include communication modules for transmitting andreceiving information over one or more direct connections (e.g., USB,Firewire, or other interface) and one or more network connections (e.g.,including a modem or other network interface device). The memory modulemay include internal memory devices and one or more external memorydevices. The intelligence module also includes a display module, such asa monitor, screen, or printer. In one aspect, the intelligence modulereceives data such as patient test results from a data acquisitionmodule such as a test system, either through a direct connection or overa network. For example, the test system may be configured to runmultianalyte tests on one or more patient samples and automaticallyprovide the test results to the intelligence module. The data may alsobe provided to the intelligence module via direct input by a user or itmay be downloaded from a portable medium such as a compact disk (CD) ora digital versatile disk (DVD). The test system may be integrated withthe intelligence module, directly coupled to the intelligence module, orit may be remotely coupled with the intelligence module over thenetwork. The intelligence module may also communicate data to and fromone or more client systems over the network as is well known. Forexample, a requesting physician or healthcare provider may obtain andview a report from the intelligence module, which may be resident in alaboratory or hospital, using a client system.

The network can be a LAN (local area network), WAN (wide area network),wireless network, point-to-point network, star network, token ringnetwork, hub network, or other configuration. As the most common type ofnetwork in current use is a TCP/IP (Transfer Control Protocol andInternet Protocol) network such as the global internetwork of networksoften referred to as the “Internet” with a capital “I,” that will beused in many of the examples herein, but it should be understood thatthe networks that the present invention might use are not so limited,although TCP/IP is the currently preferred protocol.

Several elements in the system may include conventional, well-knownelements that need not be explained in detail here. For example, theintelligence module could be implemented as a desktop personal computer,workstation, mainframe, laptop, etc. Each client system could include adesktop personal computer, workstation, laptop, cell phone, tablet, PDA,or any WAP-enabled device or any other computing device capable ofinterfacing directly or indirectly to the Internet or other networkconnection. A client system typically runs an HTTP client, e.g., abrowsing program, such as Microsoft's Internet Explorer™ browser,Google's Chrome browser, or a WAP-enabled browser or mobile applicationin the case of a cell phone, tablet, PDA, or other wireless device, orthe like, allowing a user of the client system to access, process, andview information and pages available to it from the intelligence moduleover the network. Each client system also typically includes one or moreuser interface devices, such as a keyboard, a mouse, touch screen, pen,or the like, for interacting with a graphical user interface (GUI)provided by the browser on a display (e.g., monitor screen, cell phoneor tablet screen, LCD display, etc.) in conjunction with pages, forms,and other information provided by the intelligence module. As discussedabove, the present invention is suitable for use with the Internet,which refers to a specific global internetwork of networks. However, itshould be understood that other networks can be used instead of theInternet, such as an intranet, an extranet, a virtual private network(VPN), a non-TCP/IP based network, any LAN or WAN, or the like.

According to one embodiment, each client system and all of itscomponents are operator configurable using applications, such as abrowser, including computer code run using a central processing unitsuch as an Intel® Pentium® processor or the like. Similarly, theintelligence module and all of its components might be operatorconfigurable using application(s) including computer code run using acentral processing unit such as an Intel® Pentium® processor or thelike, or multiple processor units. Computer code for operating andconfiguring the intelligence module to process data and test results asdescribed herein is preferably downloaded and stored on a hard disk, butthe entire program code, or portions thereof, may also be stored in anyother volatile or non-volatile memory medium or device as is well known,such as a ROM or RAM, or provided on any other computer readable mediumcapable of storing program code, such as a compact disk (CD) medium,digital versatile disk (DVD) medium, a floppy disk, ROM, RAM, and thelike.

The computer code for implementing various aspects and embodiments ofthe present invention can be implemented in any programming languagethat can be executed on a computer system such as, for example, in C,C++, C#, HTML, Java, JavaScript, or any other scripting language, suchas VBScript. Additionally, the entire program code, or portions thereof,may be embodied as a carrier signal, which may be transmitted anddownloaded from a software source (e.g., server) over the Internet, orover any other conventional network connection as is well known (e.g.,extranet, VPN, LAN, etc.) using any communication medium and protocols(e.g., TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known.

VII. Examples

The present invention will be described in greater detail by way ofspecific examples. The following examples are offered for illustrativepurposes, and are not intended to limit the present invention in anymanner. Those of skill in the art will readily recognize a variety ofnoncritical parameters which can be changed or modified to yieldessentially the same results.

Example 1. Prediction of the Formation of Antibodies-to-Infliximab (ATI)Based on Infliximab (IFX) Levels

This example illustrates the association between infliximab (IFX) levelsand the formation of antibodies-to-IFX (ATI) in Crohn's disease (CD)patients at various time points during the course of IFX therapy. Incertain aspects, this example shows that the level of an anti-TNFα drug(e.g., IFX) at an earlier time point during therapy is predictive ofanti-TNFα drug autoantibody (e.g., ATI) formation at a later time pointduring therapy. In other aspects, this example shows that anti-TNFα drug(e.g., IFX) levels above a specific threshold or cut-off value (i.e.,drug levels in the 4^(th) quartile or Q4 based on quartile analysis) atan earlier time point during therapy is predictive of whether a patientwill develop anti-TNFα drug autoantibody (e.g., ATI) at a later timepoint during therapy.

FIGS. 1A-1C show the relationship between IFX levels and the ATIformation in CD patients at week 2 (“t2”), week 6 (“t3”), and week 14(“t4”) following the initiation of IFX therapy (baseline or week 0 or“t1”). In particular, FIGS. 1A-1C illustrate that IFX levels at week 2(FIG. 1A), week 6 (FIG. 1B), and week 14 (FIG. 1C) can be used topredict whether or not ATI would be detected at week 14. As such, FIGS.1A-1C demonstrate that the level of an anti-TNFα drug (e.g., IFX) at anearlier time point during therapy (e.g., at week 6) is predictive ofanti-TNFα drug autoantibody (e.g., ATI) formation at a later time pointduring therapy (e.g., at week 14).

FIG. 2 shows the association between TNFα, IFX, C-reactive protein(CRP), and human serum albumin (HSA) with ATI formation (p-values) atbaseline (week 0), and at weeks 2, 6, and 14 following IFX therapy. Inparticular, FIG. 2 illustrates that only IFX levels were predictive ofATI formation after 14 weeks of therapy.

FIG. 3 shows a stratified analysis of the association between IFX levelsand ATI formation in CD patients receiving IFX monotherapy orcombination therapy with IFX and an immunosuppressive agent (e.g.,immunomodulator or “IMM”) such as azathioprine (AZA), 6-mercaptopurine(6-MP), or methotrexate (MTX). In particular, FIG. 3 illustrates thatIFX levels at weeks 2 and 6 predict ATI formation at week 14 only inpatients receiving monotherapy.

FIG. 4 shows the results of a quartile analysis that was performed tofurther characterize the association between IFX levels at week 2 andATI formation at week 14. In particular, FIG. 4 illustrates that IFXdrug levels at week 2 should be greater than 37 μg/ml (i.e., 4^(th)quartile or Q4) to prevent ATI formation at week 14, independent ofwhether the patient is receiving IFX monotherapy or combination therapy.

FIG. 5 shows the results of a quartile analysis that was performed tofurther characterize the association between IFX levels at week 6 andATI formation at week 14. In particular, FIG. 5 illustrates that IFXdrug levels at week 6 should be greater than 35 μg/ml (i.e., 4^(th)quartile or Q4) to prevent ATI formation at week 14, independent ofwhether the patient is receiving IFX monotherapy or combination therapy.

FIG. 6 shows the results of a quartile analysis that was performed tofurther characterize the association between IFX levels at week 14 andATI formation at week 14. CRP level at week 14 is presented as itsmedian. In particular, FIG. 6 illustrates that IFX drug levels at week14 should be greater than 14 μg/ml (i.e., 4^(th) quartile or Q4) toprevent ATI formation at week 14, independent of whether the patient isreceiving 1FX monotherapy or combination therapy.

Example 2. Multiple Regression Models for Predicting IFX Levels and ATIFormation

This example illustrates multiple regression modelling to predict IFXlevels and ATI formation at a later time point during a course oftherapy with IFX (e.g., at week 2, 6, or 14) in Crohn's disease (CD)patients prior to the initiation of IFX therapy. In certain aspects,this example shows that the initial dose of an anti-TNFα drug IFX) canbe individualized and tailored for each patient at the start of therapybased on the predictive models described herein. In other aspects, thisexample shows that patients predicted to produce anti-TNFα drugautoantibody (e.g., ATI) during a course of therapy with an anti-TNFαdrug (e.g., 1FX) based on the predictive models described herein can beadministered an initial dose of the drug that is increased compared tothe normal starting dose and/or an increased dose of animmunosuppressive agent (e.g., immunomodulator or “IMM”) such asazathioprine (AZA), 6-mercaptopurine (6-MP), or methotrexate (MTX).

Table 1 shows non-limiting examples of variables that were used in themultiple regression models described herein to predict IFX levels atweek 2 (“t2”), week 6 (t3″), and week 14 (“t4”) following the initiationof IFX therapy (baseline or week 0 or 11″) and to predict ATI formationat week 14 following the initiation of IFX therapy.

TABLE 1 Monotherapy Combination therapy variables Mean Std Dev Median NMean Std Dev Median N p-value age 43.78 15.22 41.00 74 38.43 14.12 35.0012.7 0.0148 Gender (Female freq) 0.60 44 0.52 66 0.3782 Age at diagnosis29.24 12.67 26.00 74 2.6.72 11.62 23.00 127 0.1635 BMP at 1st IFX 23.154.33 23.00 74 23.04 4.01 23.00 121 0.86 Age at 1st IFX (years) 39.9215.00 38.00 74 35.07 14.15 32.00 127 0.0255 TNF_T1 2.18 2.57 1.54 742.16 1.75 1.71 126 0.25 TNF_t2 6.10 5.58 4.72 71 5.. 16 3.77 4.16 1220.66 TNF_T3 8.04 7.99 5.78 74 6.83 4.34 5.93 124 0.92 TNF_t4 10.43 8.818.47 67 8.34 6.23 7.50 106 0.16 IFX Reported 25.97 12.30 24.12 71 27.2412.40 28.64 122 0.67 [μg/mL]_t2 IFX Reported 21.82 14.93 16.80 74 25.1115.45 22.75 124 0.0471 [μg/mL]_T3 IFX Reported 9.09 9.65 6.64 67 10.469.96 8.85 107 0.24 [μg/mL]_t4 CRP_w0 (mg/L) 22.11 32.27 10.65 74 18.8725.28 9.90 127 0.74 CRP_w2 4.93 6.50 2.25 74 5.99 13.08 1.60 125 0.67CRP_w6 5.29 8.18 2.00 73 4.32 6.80 1.50 127 0.34 CRP_w14 5.76 9.16 2.2067 5.51 9.41 1.60 110 0.74 Albumin_w0 (g/dL) 4.05 0.46 4.07 74 4.16 0.384.16 127 0.074 Albumin_w2 4.20 0.44 4.25 73 4.26 0.36 4.25 124 0.27Albumin_w6 4.27 0.41 4.35 70 4.35 0.38 4.37 126 0.19 Albumin_w14 4.300.34 4.31 65 4.40 0.39 4.40 107 0.1

FIG. 7 shows the results of multiple regression modelling to predict IFXlevels at week 14 using baseline (week 0 or “t1”) measures of thefollowing initial predictor variables: TNFα at t1 (TNF_T1); CRP at t1(CRP_w0 (mg/L)); albumin at t1 (Albumin_w0 (g/dL)); immunomodulator(IMM); gender; age; age at diagnosis; Body Mass Index (BMI) at 1st IFX;hemoglobin at 1st IFX; age at 1st IFX (years); and previous surgery(i.e., surgery previous to 1st 1FX). In particular, FIG. 7 illustratesthat the best model used baseline values of TNFα (i.e., Log [TNF_T1]),albumin, age, and BMI to predict drug levels at week 14 with about 16%accuracy (see, “RSquare Adj”).

FIG. 8 shows the results of multiple regression modelling to predict IFXlevels at week 2 using baseline (week 0 or “t1”) measures of thefollowing initial predictor variables: TNFα at t1 (TNF_T1); CRP at t1(CRP_w0 (mg/L)); albumin at t1 (Albumin_w0 (g/dL)); immunomodulator(IMM); gender; age; age at diagnosis; Body Mass Index (BMI) at 1st IFX;hemoglobin at 1st IFX; age at 1st IFX (years); and previous surgery. Inparticular, FIG. 8 illustrates that the best model used baseline valuesof CRP (i.e., Log [CRP_w0 (mg/L)]), albumin, gender, and BMI to predictdrug levels at week 2 with about 27% accuracy (see, “RSquare Adj”).

FIG. 9 shows the results of multiple regression modelling to predict IFXlevels at week 6 using baseline (week 0 or “t1”) and week 2 (“t2”)measures of the following initial predictor variables: TNFα at t(TNF_T1); CRP at t1 (CRP_w0 (mg/L)); albumin at t1 (Albumin_w0 (g/dL));immunomodulator (IMM) use during IFX induction; gender; age; age atdiagnosis; Body Mass Index (BMI) at 1st 1FX; hemoglobin at 1st IFX; ageat 1st 1FX (years); previous surgery; IFX at t2 (HA Reported[pg/mL]_t2); ATI at t2 (Total ATI Reported [U/ml]_T2); TNFα at t2(TNF_t2); CRP at t2 (CRP_w2); and albumin at t2 (Albumin_w2). Inparticular, FIG. 9 illustrates that the best model used baseline valuesof IMM use during IFX induction and previous surgery, and week 2 valuesof IFX (i.e., Log [IFX Reported [μg/mL]_t2]) and CRP (i.e., Log[CRP_w2]) to predict drug levels at week 6 with about 40% accuracy (see,“RSquare Adj”).

FIG. 10 shows the results of multiple regression modelling to predictIFX levels at week 14 using baseline (week 0 or “t1”), week 2 (“t2”),and week 6 (“t3”) measures of the following initial predictor variables:TNFα at t1 (TNF_T1); CRP at t1 (CRP_w0 (mg/L)); albumin at t1(Albumin_w0 (g/dL)); immunomodulator (IMM) use during IFX induction;gender; age; age at diagnosis; Body Mass Index (BMI) at 1st IFX;hemoglobin at 1st IFX; age at 1st IFX (years); previous surgery; IFX att2 (IFX Reported [μg/mL]_t2); ATI at t2 (Total ATI Reported [U/ml]_T2);TNFα at t2 (TNF_t2); CRP at t2 (CRP_w2); albumin at t2 (Albumin_w2); IFXat t3 (IFX Reported [μg/mL]_T3); ATI at t3 (Total ATI Reported[U/ml]_T3); TNFα at t3 (TNF_T3); CRP at t3 (CRP_w6); and albumin at t3(Albumin_w6). In particular, FIG. 10 illustrates that the best modelused baseline values of age at 1st IFX (years), week 2 values of IFX(i.e., Log [IFX Reported [μg/mL]_t2]), and week 6 values of IFX (i.e.,Log [IFX Reported [μg/mL]_T3]), total ATI, and CRP (i.e., Log [CRP_w6])to predict drug levels at week 14 with about 51.1% accuracy (see,“RSquare Adj”).

FIG. 11 shows the results of multiple regression modelling to predictIFX levels at week 14 using baseline (week 0 or “t1”), week 2 (“t2”),and week 6 (“t3”) measures of the same initial predictor variablesdescribed for FIG. 10 , but enforcing TNFα in the model. In particular,FIG. 11 illustrates that the best model used baseline values of age at1st IFX (years), week 2 values of IFX (i.e., Log [IFX Reported[μg/mL]_t2]), and week 6 values of IFX (i.e., Log [IFX Reported[μg/mL]_T3]), total ATI, TNFα (i.e., Log [TNF_T3]), and CRP (i.e., Log[CRP_w6]) to predict drug levels at week 14 with about 51.2% accuracy(see, “RSquare Adj”).

FIG. 12 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using baseline (week 0 or “t1”)measures of the following initial predictor variables: TNFα at t1(TNF_T1); CRP at t1 (CRP_w0 (mg/L)); albumin at t1 (Albumin_w0 (g/dL));immunomodulator (IMM); gender; age; age at diagnosis; Body Mass Index(BMI) at 1st IFX; hemoglobin at 1st IFX; age at 1st IFX (years); andprevious surgery. In particular, FIG. 12 illustrates that the best modelused baseline values of TNFα (i.e., Log [TNF_T1]), gender, hemoglobin at1st IFX, and IMM use during IFX induction to predict ATI formation atweek 14 with about 72% accuracy (see, AUC value).

FIG. 13 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using baseline (week 0 or “t1”) andweek 2 (“t2”) measures of the following initial predictor variables:TNFα at t1 (TNF_T1); CRP at t1 (CRP_w0 (mg/L)); albumin at t1(Albumin_w0 (g/dL)); immunomodulator (IMM) use during IFX induction;gender; age; age at diagnosis; Body Mass Index (BMI) at 1st IFX;hemoglobin at 1st IFX; age at 1st IFX (years); previous surgery; TNFα att2 (TNF_t2); CRP at t2 (CRP_w2); albumin at t2 (Albumin_w2); and IFX att2 (IFX Reported [μg/mL]_t2). In particular, FIG. 13 illustrates thatthe best model used baseline values of TNFα (i.e., Log [TNF_T1]), IMMuse during IFX induction, gender, and hemoglobin at 1st IFX, and week 2values of albumin and IFX (i.e., Log [IFX Reported [μg/mL]_t2]) topredict ATI formation at week 14 with about 76% accuracy (see, AUCvalue).

FIG. 14 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using baseline (week 0 or “t1”), week 2(“t2”), and week 6 (“t3”) measures of the following initial predictorvariables: TNFα at t1 (TNF_T1); CRP at t1 (CRP_w0 (mg/L)); albumin at t1(Albumin_w0 (g/dL)); immunomodulator (IMM) use during IFX induction;gender; age; age at diagnosis; Body Mass Index (BMI) at 1st IFX;hemoglobin (Hb) at 1st IFX; age at 1st IFX (years); previous surgery;IFX at t2 (IFX Reported [μg/mL]_t2); ATI at t2 (Total ATI Reported[U/ml]_T2); TNFα at t2 (TNF_t2); CRP at t2 (CRP_w2); albumin at t2(Albumin_w2); IFX at t3 (IFX Reported [μg/mL]_T3); ATI at t3 (Total ATIReported [U/ml]_T3); TNFα at t3 (TNF_T3); CRP at t3 (CRP_w6); andalbumin at t3 (Albumin_w6). In particular, FIG. 14 illustrates that thebest model used baseline values of TNFα (i.e., Log [TNF_T1]), gender,and hemoglobin at 1st IFX, and week 6 values of albumin and IFX (i.e.,Log [IFX Reported [μg/mL]_T3]) to predict ATI formation at week 14 withabout 78% accuracy (see, AUC value).

FIG. 15 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using all time point measurements(i.e., baseline (week 0 or “t1”), week 2 (“t2”), week 6 (“t3”), and week14 (“t4”)) of the following initial predictor variables: TNFα at t1(TNF_T1); CRP at t1 (CRP_w0 (mg/L)); albumin at t1 (Albumin_w0 (g/dL));immunomodulator (IMM) use during IFX induction; gender; age; age atdiagnosis; Body Mass Index (BMI) at 1st IFX; hemoglobin (Hb) at 1st IFX;age at 1st IFX (years); previous surgery; IFX at t2 (IFX Reported[μg/mL]_t2); ATI at t2 (Total ATI Reported [U/ml]_T2); TNFα at t2(TNF_t2); CRP at t2 (CRP_w2); albumin at t2 (Albumin_w2); IFX at t3 (IFXReported [μg/mL]_T3); ATI at t3 (Total ATI Reported [U/ml]_T3); TNFα att3 (TNF_T3); CRP at t3 (CRP_w6); albumin at t3 (Albumin_w6); TNFα at t4(TNF_T4), IFX at t4 (IFX Reported [μg/mL]_t4); CRP at t4 (CRP_w14); andalbumin at t4 (Albumin_w14). In particular, FIG. 15 illustrates that thebest model used baseline values of CRP (i.e., Log [CRP_w0 (mg/L)]),gender, and hemoglobin at 1st IFX, week 2 values of TNFα (i.e., Log[TNF_t2]), week 6 values of albumin, and week 14 values of TNFα (i.e.,Log [TNF_t4]), IFX (i.e., Log [IFX Reported [μg/mL]_t4]), and CRP (i.e.,Log [CRP_w14]) to predict ATI formation at week 14 with about 95%accuracy (see, AUC value).

FIG. 16 shows the results of multiple logistic regression modelling topredict ATI formation at week 14 using all time point measurements(i.e., baseline (week 0 or “t1”), week 2 (“t2”), week 6 (“t3”), and week14 (“t4”)) of the following initial predictor variables: TNFα at t1(TNF_T1); CRP at t1 (CRP_w0 (mg/L)); albumin at t1 (Albumin_w0 (g/dL));immunomodulator (IMM) use during IFX induction; gender; age; age atdiagnosis; Body Mass Index (BMI) at 1st IFX; hemoglobin (Hb) at 1st IFX;age at 1st IFX (years); previous surgery; TNFα/IFX ratio at t2 (TNF2/IFXReported [μg/mL]_t2); ATI at t2 (Total ATI Reported [U/ml]_T2); TNFα att2 (TNF_t2); CRP at t2 (CRP_w2); albumin at t2 (Albumin_w2); TNFα/IFXratio at t3 (TNF3/IFX Reported [ag/mL]_T3); ATI at t3 (Total ATIReported [U/ml]_T3); CRP at t3 (CRP_w6); albumin at t3 (Albumin_w6);TNFα/IFX ratio at t4 (TNF4/IFX Reported [μg/mL]_t4); CRP at t4(CRP_w14); and albumin at t4 (Albumin_w14). In particular, FIG. 16illustrates that the best model used baseline values of CRP (i.e., Log[CRP_w0 (mg/L)]), gender, and hemoglobin at 1st IFX, week 2 values ofTNFα (i.e., Log [TNF_t2]), week 6 values of albumin, and week 14 valuesof TNFα/IFX ratio (i.e., Log [TNF4/IFX4]) and CRP (i.e., Log [CRP_w14])to predict ATI formation at week 14 with about 94% accuracy (see, AUCvalue).

Example 3. Prediction of IFX Levels and ATI Formation Based on TNFαLevels

This example illustrates the association between TNFα levels and IFXlevels, ATI formation, human serum albumin (HSA) levels, and C-reactiveprotein (CRP) levels during the course of IFX therapy and at baselineprior to the initiation of therapy.

FIGS. 17A-17D show the relationship between TNFα levels at baseline(FIG. 17A), week 2 (FIG. 17B), week 6 (FIG. 17C), and week 14 (FIG. 17D)and ATI formation at week 14.

FIG. 18 shows the association between TNFα levels at baseline and IFXlevels at weeks 2, 6, and 14. In particular, FIG. 18 illustrates thatTNFα levels at baseline predict IFX levels at week 14.

FIG. 19 shows a stratified analysis of the association between baselineTNFα levels and IFX levels in CD patients receiving IFX monotherapy orcombination therapy with IFX and an immunosuppressive agent. Inparticular, FIG. 19 illustrates that TNFα levels at baseline predict IFXlevels at week 14 in patients receiving monotherapy.

FIG. 20 shows the association between HSA levels and TNFα levels. Inparticular, FIG. 20 illustrates that there is an inverse relationshipbetween HSA levels and TNFα levels at weeks 0, 2, and 6.

FIG. 21 shows the association between CRP levels and TNFα levels. Inparticular, FIG. 21 illustrates that there is an association betweenbaseline CRP levels and baseline TNFα levels.

Example 4. Prediction of CRP Levels and ATI Formation Based on Ratios ofTNF, to IFX Levels

This example illustrates the association between TNFα/IFX ratios andC-reactive protein (CRP) levels and ATI formation during the course ofIFX therapy and at baseline prior to the initiation of therapy.

FIG. 22 shows the association between TNFα/IFX ratios and CRP levels. Inparticular, FIG. 22 illustrates that ratios of TNFα/IFX at weeks 2, 6,and 14 predict CRP levels at week 14.

FIG. 23 shows a stratified analysis of the association between ratios ofbaseline TNFα levels to IFX levels at different time points and CRPlevels at week 14 in CD patients receiving IFX monotherapy orcombination therapy with IFX and an immunosuppressive agent. Inparticular, FIG. 23 illustrates that ratios of baseline TNFα levels toIFX levels at week 6 predict CRP levels at week 14 in patients receivingcombination therapy.

FIG. 24 shows a stratified analysis of the association between ratios ofTNFα levels to IFX levels at different time points and CRP levels atweek 14 in CD patients receiving IFX monotherapy or combination therapywith IFX and an immunosuppressive agent. In particular, FIG. 24illustrates that ratios of TNFα levels to IFX levels at week 14 predictCRP levels at week 14 in patients receiving combination therapy.

FIG. 25 shows a stratified analysis of the association between ratios ofTNFα levels to IFX levels at different time points and ATI formation atweek 14 in CD patients receiving IFX monotherapy or combination therapywith IFX and an immunosuppressive agent. In particular, FIG. 25illustrates that ratios of TNFα levels to IFX levels at week 6 predictATI formation at week 14 in patients receiving monotherapy and ratios ofTNFα levels to IFX levels at week 14 predict ATI formation at week 14 inpatients receiving monotherapy or combination therapy.

Example 5. Prediction of CRP Levels Based on IFX Levels

This example illustrates the association between IFX levels and CRPlevels. In particular, FIG. 26 shows that there is an inverserelationship between IFX levels and CRP levels at weeks 0, 2, and 6during the course of therapy. As such, IFX levels at weeks 2, 6, and 14predict CRP levels at week 14.

Example 6. Prediction of IFX and CRP Levels Based on HSA Levels

This example illustrates the association between baseline human serumalbumin (HSA) levels and IFX levels during the course of therapy. Inparticular, FIG. 27 shows that there is an association between baselineHSA levels and IFX levels at weeks 2, 6, and 14. As such, baseline HSAlevels predict IFX levels during the course of therapy.

This example also illustrates the association between CRP levels and HSAlevels at baseline and at different time points during the course oftherapy. In particular, FIG. 28 shows that there is an inverserelationship between CRP levels and HSA levels at baseline and at weeks2, 6, and 14. FIG. 28 also shows that HSA levels at baseline predict CRPlevels at week 14.

Example 7. Biomarkers for Predicting Clinical Outcome and ATI Formation

This example illustrates that biomarkers such as IL12p40, IL-8, and IFXat certain time points during the course of IFX therapy are associatedwith clinical outcome. This example also illustrates that biomarkerssuch as IL-8 and IFX at certain time points during the course of IFXtherapy are associated with ATI formation at a later time point.

The IFX dosing scheme for the ulcerative colitis (UC) patients enrolledin this study was as follows: Week 0=24 hours after dosing (TO); Week2=before 2^(nd) infusion (T5); and Week 6=before 3^(rd) infusion (T9).Clinical outcome was defined as the endoscopic response at week 8. Therewere 8 non-responders and 11 responders in the patient cohort. Thefollowing biomarkers were assayed in patient samples: IFN-g, IL-B, IL-2,IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, GMCSF, IL12p40, IFX, TNFα, andATI. The following time points were considered for analysis: TO, T5, andT9. As described in this example, IL12p40 levels at T5, IL-8 levels atT5, and IFX levels at TO (1^(st) dose) were associated with clinicaloutcome, while IL-8 levels at T5 and IFX levels at TO were associatedwith ATI formation at T9 (i.e., within the first 6 weeks of IFXtherapy).

FIG. 29 shows the association between IL12p40 levels at T5 andendoscopic response at week 8. In particular, elevated levels of IL12p40at T5 (week 2) were associated with non-response at week 8. Theseresults illustrate that inflammation is also driven by IL12p40, not justTNFα. Patients with elevated IL12p40 levels should be administeredcombination therapy with an anti-IL12p40 drug such as Stelara®(ustekinumab) and an anti-TNFα drug. These results also illustrate thatIL levels at week 2 can be used to predict clinical outcome (e.g.,endoscopic response) at week 8. Similarly, the consistent trend observedin the data indicates that IL12p40 levels at T9 (week 6) can be used topredict clinical outcome (e.g., endoscopic response) at week 16.

FIG. 30 shows the association between IL-8 levels at T5 and endoscopicresponse at week 8. In particular, elevated levels of IL-8 at T5 (week2) were associated with non-response at week 8. These results illustratethat IL-8 levels at week 2 can be used to predict clinical outcome(e.g., endoscopic response) at week 8. Similarly, the consistent trendobserved in the data indicates that IL-8 levels at T9 (week 6) can beused to predict clinical outcome (e.g., endoscopic response) at week 16.

FIG. 31 shows the association between IFX drug levels at TO andendoscopic response at week 8. In particular, low levels of IFX at TO(24 hours after dosing) were associated with non-response at week 8.These results illustrate that IFX levels 24 hours after dosing can beused to predict clinical outcome (e.g., endoscopic response) at week 8.

FIG. 32 shows the results of multiple regression modelling to predictclinical outcome (e.g., endoscopic response) at week 8. In particular,FIG. 32 illustrates that using IL12p40 and IL-8 levels at T5 (week 2) asthe predictor variables provided a prediction of endoscopic response atweek 8 with an area-under-the-curve (AUC) of 0.95.

FIG. 33 shows the association between IL-8 levels at T5 and ATIformation at T9. In particular, elevated levels of IL-8 at T5 (week 2)were associated with ATI formation at T9 (i.e., by week 6 or within thefirst 6 weeks of IFX therapy). These results illustrate that IL-8 levelsat week 2 can be used to predict ATI formation by week 6.

FIG. 34 shows the association between IFX levels at TO and ATI formationat T9. In particular, low levels of IFX at TO (24 hours after dosing)were associated with ATI formation at T9 (i.e., by week 6 or within thefirst 6 weeks of IFX therapy). These results illustrate that IFX levels24 hours after dosing can be used to predict ATI formation by week 6.

FIG. 35 shows the association between the ratio of TNFα levels to IFXlevels (i.e., TNFα/IFX ratio) at TO and ATI formation at T9. Inparticular, higher TNFα/IFX ratios at TO (24 hours after dosing) wereassociated with ATI formation at T9 (i.e., by week 6 or within the first6 weeks of IFX therapy). These results illustrate that determining aratio of TNFα/IFX levels 24 hours after dosing can be used to predictATI formation by week 6.

FIG. 36 shows the results of multiple regression modelling to predictATI formation at T9. In particular, FIG. 36 illustrates that the use ofIL-8 levels at T5 (week 2) together with IFX levels at TO (24 hoursafter dosing) as the predictor variables was capable of predicting ATIformation by week 6 (i.e., within the first 6 weeks of IFX therapy).

FIG. 37 shows the results of multiple regression modelling to predictATI formation at T9. In particular, FIG. 37 illustrates that the use ofIL-8 levels at T5 (week 2) together with the ratio of TNFα levels to IFXlevels (i.e., TNFα/IFX ratio) at TO (24 hours after dosing) as thepredictor variables provided a prediction of ATI formation by week 6(i.e., within the first 6 weeks of IFX therapy) with anarea-under-the-curve (AUC) of 0.904.

Accordingly, this example demonstrates that IL-8 and IL12p40 areassociated with endoscopic response at week 8, and that IL-8 and IFXlevels predict ATI formation at T9. Notably, this example shows thatIL-8 is an important predictor for both endoscopic response as well asATI formation.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, one of skill in the art will appreciate that certainchanges and modifications may be practiced within the scope of theappended claims. In addition, each reference provided herein isincorporated by reference in its entirety to the same extent as if eachreference was individually incorporated by reference.

1. A method for predicting whether a subject will develop autoantibodiesto an anti-TNFα drug at a later time point during a course of therapywith the anti-TNFα drug, the method comprising measuring the level ofthe anti-TNFα drug in a sample obtained from the subject at an earliertime point during the course of therapy.
 2. A system for predictingwhether a subject will develop autoantibodies to an anti-TNFα drug at alater time point during a course of therapy with the anti-TNFα drug, thesystem comprising: (a) a data acquisition module configured to produce adata set comprising one or more predictor variables for the subjectdetermined at an earlier time point during the course of therapy and/orprior to the initiation of the course of therapy; (b) a data processingmodule configured to process the data set by applying a statisticalanalysis to the data set to produce a statistically derived decisionpredicting whether the subject will develop autoantibodies to theanti-TNFα drug based upon the one or more predictor variables; and (c) adisplay module configured to display the statistically derived decision.3. A method for predicting the level of an anti-TNFα drug in a subjectat a later time point during a course of therapy with the anti-TNFαdrug, the method comprising determining one or more predictor variablesfor the subject at an earlier time point during the course of therapyand/or prior to the initiation of the course of therapy.